If you're looking into AI for customer support, you've almost certainly heard of Decagon and Sierra. They're two of the biggest, most well-funded names out there, both promising to transform your customer experience with smart, autonomous agents.
But when you get past the marketing buzz, you’ll find they take very different roads to get you there. Choosing between them isn't just about features; it's about how much time you'll spend setting things up, how much you'll rely on your engineering team, and whether you can even predict your monthly bill.
This guide cuts through the noise in the Decagon vs Sierra debate. We'll help you figure out which tool actually solves your problems instead of creating a whole new set of them.
What is Decagon?
Decagon has made a splash with its AI agent platform that lets customer experience teams build their own complex workflows using plain English. It's designed for big companies that need automation that can do more than just answer simple questions.
Their big idea is something called Agent Operating Procedures (AOPs). The goal is to let CX teams write out instructions for the AI (like how to process a refund), while developers set up code-based rules in the background to keep things on track. On paper, this sounds great, it gives more power to the support team. But it’s not exactly a no-code dream. Your engineers still have to build all the core integrations and logic before your CX team can write a single word.
Decagon has some impressive stats, with clients like Hertz apparently seeing deflection rates over 70%. It's positioned as a heavy-duty solution for companies wanting to stop paying for expensive consultants for every minor tweak.
What is Sierra?
Sierra is another big name in this space, founded by some high-profile people, including the ex-co-CEO of Salesforce, Bret Taylor. They're a direct competitor to Decagon and are also targeting large companies with hefty budgets.
Sierra's platform is broken into two pieces: the Agent SDK and the Agent Studio. The SDK (Software Development Kit) is where your developers write the code that defines the AI’s logic, connects to your systems, and builds its "skills." The Agent Studio is the slick, user-friendly dashboard where your CX team can then adjust the AI’s tone of voice, fine-tune conversations, and keep an eye on performance.
This split keeps things organized, but it also means you're heavily reliant on your engineering team for anything more than small adjustments. With its massive valuation, Sierra is clearly a premium, developer-first choice for businesses that want a deeply customized AI agent and have the technical crew to build and manage it.
The path to going live
All the features in the world don't mean much if you can't get the tool up and running. This is where the glossy sales demos often bump up against reality.
Decagon's hands-on approach with AOPs
Decagon’s promise of using natural language is definitely attractive, but it’s important to know that AOPs aren't truly "no-code." Before your CX team can do their thing, your engineering team has a lot of groundwork to do. They need to connect your systems, configure APIs, and write the code that acts as a safety net for the AI. That means you’re still facing an implementation that can easily take weeks, if not months, of planning and development.
Sierra's reliance on developers
Sierra's model leans even more heavily on your technical folks. The Agent Studio looks easy to use, but the AI's real brain is in the SDK. Need the agent to do something new, like pull shipping info from a different carrier? Your CX team can't handle that. A developer has to jump back into the code, build the new skill, test it, and push it live. This creates a classic bottleneck and slows you down, keeping control away from the people who actually talk to your customers.
The reality for support teams: You need results now
Let's be honest: both platforms demand a huge upfront investment of engineering time, long planning meetings, and buy-in from half the company. For most support teams who are already underwater and need to lower their ticket volume yesterday, a project that takes months to launch just isn't an option.
Beyond Decagon vs Sierra: Go live in minutes, not months
What if you could get the same power without the painful setup process? For teams that care about speed and independence, there's another way. Tools like eesel AI are built to be completely self-serve. You can sign up, connect your knowledge sources, and have a working AI agent running without talking to a single salesperson.
With a one-click helpdesk integration, you can instantly link tools like Zendesk or Intercom. eesel AI fits into your current workflow instead of forcing you into a massive "rip and replace" project. It’s a practical tool that starts showing its value on day one, not next quarter.
Customization and control
Once your AI is live, you need to be able to tweak its behavior as your business evolves. How much real control do Decagon and Sierra actually hand over to your frontline teams?
The problem with the 'one-bot-to-rule-them-all' mindset
A common pitfall with AI tools, as the Reddit thread pointed out, is the "single-agent mindset." This is where bots are built as one giant, all-or-nothing system. Both Decagon and Sierra can fall into this trap. Their agents are designed to handle everything at once, which makes it tough to test new ideas or roll out automation in stages. What if you just want to automate password resets to start? With these platforms, that simple task can turn into a huge, over-engineered project.
Changing the AI's logic and personality
Let's say your customers find the AI's tone a bit off, or a new issue starts flooding your queue. With Decagon’s AOPs or Sierra's SDK, making those changes often means going back to square one with another technical project. That’s not exactly agile, and it stops your CX team from quickly updating the AI based on what customers are actually saying.
Real control: Starting small and testing safely
A modern AI platform should let you roll things out gradually. This is where eesel AI really stands out. It gives you selective automation, so you can pick exactly which types of tickets the AI should handle. You can start with the easy, repetitive stuff and have it safely pass everything else to your human agents.
Even better, eesel AI has a simulation mode that lets you test your setup on thousands of your own past tickets. You can see exactly how it would have replied, get solid forecasts on its performance, and feel 100% confident before it ever talks to a real customer. And a simple prompt editor lets you define the AI's tone, personality, and even create custom actions without touching a line of code.
The bottom line: A look at pricing
Alright, let's talk about what might be the most important factor for your team: the cost. How much will these platforms set you back, and is the bill even predictable?
The enterprise pricing playbook
It’s probably not a shock that neither Decagon nor Sierra lists their prices online. It’s the classic enterprise playbook, and it tells you a few things right away:
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Get ready for a long sales process and a custom quote.
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The price tag will be high, likely with big setup fees and multi-year contracts.
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It's out of reach for most small and mid-sized businesses.
Decagon reportedly has two pricing models: per-conversation or per-resolution. The per-resolution option sounds good at first, but it has a big catch. It can lead to unpredictable bills and, in a way, punishes you for being successful. The more tickets your AI resolves, the more you pay.
The problem with unpredictable bills
For any support manager trying to stick to a budget, usage-based fees are a headache. A random spike in customer questions could blow up your invoice, making it nearly impossible to plan your finances. You almost end up hoping your automation isn't too effective.
Why transparent, predictable pricing wins
This is where a clear, straightforward option like eesel AI makes a world of difference.
eesel AI offers simple, clear plans based on the features and volume you need.
| Plan | Effective /mo (Annual) | AI Interactions/mo | Key Features |
|---|---|---|---|
| Team | $239 | Up to 1,000 | Train on docs, Slack integration, AI Copilot |
| Business | $639 | Up to 3,000 | Train on past tickets, MS Teams, AI Actions, Simulation |
| Custom | Contact Sales | Unlimited | Advanced orchestration, custom integrations, advanced security |
The most important part? eesel AI has no per-resolution fees. You get a predictable bill and can even start with a flexible monthly plan you can cancel anytime. It takes the financial risk out of the equation for teams wanting to get started with AI.
Decagon vs Sierra: Power isn't the only thing that matters
Decagon and Sierra are, without a doubt, powerful platforms backed by a lot of money. They're pushing what's possible with enterprise AI. If you're a massive company with a big budget and an engineering team ready for a long-term project, they're worth a look.
But for most support teams out there, raw power isn't everything. They need a tool that's fast, flexible, easy for them to control, and doesn't come with a surprise bill at the end of the month. For those teams, there’s a much better way forward.
This video explores how a new AI startup is competing with major players like Sierra and Decagon in the AI industry.
Get enterprise power with self-serve simplicity
Modern AI doesn't have to be a six-month headache that drains your engineering resources. You can get amazing automation and make your team more efficient with a tool that's actually built for the people using it every day.
Ready to see how fast you can launch a smart AI agent that learns from your existing knowledge? Get started with eesel AI for free and go live in minutes.
Frequently asked questions
Both Decagon and Sierra require significant upfront engineering work for integrations and logic, often leading to implementation timelines of weeks or even months. This can be a major hurdle for teams needing immediate relief.
While Decagon's AOPs aim to empower CX teams with natural language, engineers must still build the core integrations. Sierra relies heavily on its SDK, making developers essential for any significant changes beyond basic fine-tuning.
Decagon and Sierra are primarily designed for large enterprises with substantial budgets and dedicated engineering resources. Their high cost, custom sales processes, and complex implementations typically put them out of reach for most SMBs.
Neither platform lists transparent pricing online, indicating high enterprise-level costs and custom quotes. Decagon reportedly uses per-conversation or per-resolution models, with the latter potentially leading to unpredictable bills.
Both Decagon and Sierra demand substantial involvement from your engineering team, particularly for initial setup and building new capabilities or integrations. This can create bottlenecks and slow down responsiveness to evolving customer needs.
The blog suggests both Decagon and Sierra can fall into a "single-agent mindset," making it difficult to roll out automation gradually or test small changes. They are generally designed as comprehensive, all-encompassing solutions.
Yes, platforms like eesel AI offer a self-serve approach, allowing you to launch a functional AI agent in minutes by connecting existing knowledge sources and helpdesk integrations. This provides enterprise-level power without the complex setup.
